{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting programming language mentions in astronomy papers\n",
    "==========================================================\n",
    "\n",
    "\"Q1 2021 Edition\" Author: Juan Nunez-Iglesias.\n",
    "\n",
    "Adapted from code [written by](https://nbviewer.jupyter.org/github/astrofrog/mining_acknowledgments/blob/master/Mining%20acknowledgments%20in%20ADS.ipynb)\n",
    "[Thomas P. Robitaille](http://mpia.de/~robitaille/)\n",
    "and\n",
    "[updated by](https://nbviewer.jupyter.org/github/ChrisBeaumont/adass_proceedings/blob/master/Mining%20acknowledgments%20in%20ADS.ipynb)\n",
    "[Chris Beaumont](https://chrisbeaumont.org/).\n",
    "\n",
    "This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported](http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en_US) License.\n",
    "\n",
    "![license](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)\n",
    "\n",
    "### Changes since 2016\n",
    "\n",
    "In April 2018, ADS [changed](http://adsabs.github.io/blog/updates) the default search behaviour to be metadata-only, so to get the original behaviour, I had to prepend 'full:' to the search query. Thanks to [Russell Anderson](https://www.monash.edu/science/schools/physics/about/ourpeople/anderson) and [Andy Casey](http://astrowizici.st/) for prompting me and helping me debug this. Andy in particular prompted me to add tqdm support, which makes the query step a lot nicer. =)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The story so far\n",
    "\n",
    "In 2014 I came across [this tweet](https://twitter.com/BeaumontChris/status/517412133181865984) by Chris Beaumont, showing Python overtaking Matlab and rapidly gaining ground on IDL in astronomy.\n",
    "\n",
    "I'd referred to that plot a couple of times in the past, but [in 2016](https://github.com/jni/programming-languages-in-astronomy/blob/1991f7db48f7748492d3593fe6e142678cc951d1/programming-languages-in-ADS.ipynb) I wanted to use it in a PyCon-Au talk, so I thought it was time to update it. Hence, this notebook.\n",
    "\n",
    "Jake VanderPlas updated the notebook [last year](https://mobile.twitter.com/__mharrison__/status/865612360886607872), showing an even more exilarating climb.\n",
    "\n",
    "What does the story look like in March 2021? Well, it's not slowing down!\n",
    "\n",
    "Overall, all three languages are climbing, reflecting the increased importance of computation in Astronomy. But Python's lead is widening dramatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import everything we need. You can install it all using either conda or pip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime, date\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_palette('colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set some nice Matplotlib defaults. Note that there is a deprecation warning when setting the default color cycle, but I can't be bothered tracking down the fix. (It is not the simple replacement suggested by the deprecation message.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (9,6)\n",
    "mpl.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we import the [`ads` Python library](https://pypi.python.org/pypi/ads), which simplifies queries to the [Astrophysics Data System](https://ui.adsabs.harvard.edu). (The original notebooks used `requests` to create direct JSON queries, but the API appears to have changed in the meantime. I hope that using the `ads` library, someone else will take care of keeping the API queries up to date.)\n",
    "\n",
    "To run this notebook, **you need to get a free API key** to allow queries to the ADS system. Create an account at [ADS](https://ui.adsabs.harvard.edu), log in, and then look for \"Generate a new key\" under your user profile.\n",
    "\n",
    "Then, copy that key into a file called `.ads/dev_key` in your home directory. (You can also pass it as a string using the `token=<my_key>` keyword argument to `SearchQuery`, below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ads as ads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ADS system has a daily limit to the number of queries you can perform with a given key (I think 5,000, as of this writing). So that you're not wasting queries while you're developing, you can use the `ads.sandbox` package that will return mock results to your queries. Uncomment the following cell to use the sandbox instead of the real API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the following line to use the sandbox\n",
    "# import ads.sandbox as ads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we write a function that will count up how many results an individual query and year return, as well as a function to combine related queries (such as 'MATLAB' and 'Matlab')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_results(qstring):\n",
    "    q = ads.SearchQuery(q=qstring)\n",
    "    q.execute()\n",
    "    return int(q.response.numFound)\n",
    "\n",
    "\n",
    "def correct_partial_year(count, year):\n",
    "    now = datetime.now().timetuple()\n",
    "    if year == now.tm_year:\n",
    "        days_in_year = date(year, 12, 31).timetuple().tm_yday\n",
    "        corrected = count * days_in_year / now.tm_yday\n",
    "        return corrected\n",
    "    else:\n",
    "        return count\n",
    "\n",
    "\n",
    "def yearly_counts(query='', years=(2000, 2022)):\n",
    "    \"\"\"Count papers\"\"\"\n",
    "    if type(query) == list:\n",
    "        query = str.join(' OR ', query)\n",
    "    results = []\n",
    "    for year in tqdm(range(*years),\n",
    "                              desc='years', leave=False):\n",
    "        # the 'full:' modifier ensures full-text search\n",
    "        mentions = count_results(f'full:({query}) year:{year} '\n",
    "                                  'database:astronomy property:refereed')\n",
    "        mentions_corrected = correct_partial_year(mentions, year)\n",
    "        total = count_results(f'year:{year} database:astronomy '\n",
    "                               'property:refereed')\n",
    "        total_corrected = correct_partial_year(total, year)\n",
    "        results.append([year, mentions_corrected, total_corrected])\n",
    "    return np.array(results, dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, create a dictionary mapping languages to queries. I've left some of the original queries commented out, but you can uncomment them if you care about those languages in astronomy.\n",
    "\n",
    "As a side note, a simple measure of how annoying a language's name is is given by the number of queries necessary to find its mentions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a regular dictionary since they are insertion-ordered since Py3.6\n",
    "languages = {\n",
    "    'IDL': ['IDL'],\n",
    "    'Matlab': ['matlab'],  # lowercase -> case-insensitive\n",
    "    'Python': ['python'],\n",
    "    #'Fortran': ['fortran'],\n",
    "    #'C': ['C programming language', 'C language',\n",
    "    #      'C code', 'C library', 'C module'],\n",
    "    #'R': ['R programming language', 'R language',\n",
    "    #      'R code', 'R library', 'R module'],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell runs the queries. Don't waste those API hits!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e61ef3befd74f3faae64f6dfee387af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='languages', max=3.0, style=ProgressStyle(description_widt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='years', max=22.0, style=ProgressStyle(description_width='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='years', max=22.0, style=ProgressStyle(description_width='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='years', max=22.0, style=ProgressStyle(description_width='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "results = {name: yearly_counts(query)\n",
    "           for name, query\n",
    "           in tqdm(languages.items(), desc='languages')}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, define a function to plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trendlines(queries, norm=False):\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    for lang in languages:\n",
    "        counts = queries[lang]\n",
    "        x = counts[:, 0]\n",
    "        y = np.copy(counts[:, 1])\n",
    "        if norm:\n",
    "            y /= counts[:, 2]\n",
    "        ax.plot(x, y * 100, label=lang, lw=4, alpha=0.8)\n",
    "    ax.set_xlim(np.min(x), np.max(x)+0.5)\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('Percent of Refereed\\nPublications Mentioning')\n",
    "    ax.legend(loc='upper left', frameon=False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 344,
       "width": 344
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trendlines(results, norm=True)\n",
    "plt.tight_layout()\n",
    "plt.savefig('python-vs-matlab-vs-IDL-in-astro.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There you have it: some time in early 2015, Python overtook IDL as the most mentioned (and probably the most used) programming language in astronomy. Since then, the Python curve has gotten even steeper, with a silght blip in 2020, which is interesting to say the least...!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}